Analysis of two monarch populations, East and West:
- Differences in flight performance
- Correlation in the genetic profiles to check for distinction in the two populations
2022-05-09
Analysis of two monarch populations, East and West:
This is the final joined dataset we used for our analysis
## # A tibble: 6 × 21 ## ID Gene_379 Gene_203 Gene_C2 Gene_C4 Gene_C5 Gene_C7 Sex Population ## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> ## 1 FL35 -5.74 -5.53 -4.80 -4.46 -4.42 -4.77 F east ## 2 FL72 -6.21 -6.37 -4.73 -4.46 -4.46 -5.02 F east ## 3 FL74 -4.86 -4.59 -2.65 -2.61 -2.90 -2.84 F east ## 4 FL75 -6.14 -6.31 -4.84 -4.34 -4.44 -4.87 F east ## 5 FL79 -4.40 -4.18 -3.01 -2.77 -3.18 -2.98 F east ## 6 FL05 -6.00 -6.18 -4.94 -4.79 -4.25 -4.71 M east ## # … with 12 more variables: premass <dbl>, postmass <dbl>, weightloss <dbl>, ## # time.sec <dbl>, time.min <dbl>, distance <dbl>, averagevelocity <dbl>, ## # maxvelocity <dbl>, power <dbl>, Area <dbl>, PC1_wing_size <dbl>, ## # PC2_wing_shape <dbl>
plots = map(numeric_ones,
~datadistribution_plot("Population",
.,
my_data_clean))
Fig.2-Violin plot of average velocity
Fig.3-Violin plot of wing shape
We performed T-test on flight performance variables and observed the following:
Fig.5-Box plots of gene expression
Fig.6-Heat maps of gene expression
Logistic regression to determine significance of gene expression on populations
PCA population
PCA distance class
From our analysis we can conclude the following about the Eastern and Western Monarchs: